Customer feedback insight and prioritization workspace

For

Product teams collecting feedback and research data across channels

Solves

Feedback and research data sit in separate tools, so teams cannot see themes, sentiment or priorities without manual consolidation.

Delivers

Reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual consolidation while keeping every insight traceable to its source.

1. Aggregate feedback from email, web forms, support tickets and review sites.
2. Collect feedback automatically from connected channels.
3. Cluster feedback into themes with AI assistance.
4. Score sentiment and satisfaction per item and theme.
5. Summarize interviews, documents and feedback threads.
6. Transcribe audio and video with speaker detection.
7. Highlight key moments in recordings and long documents.
8. Redact sensitive or confidential information before analysis.
9. Search across all sources with filters and saved queries.
10. Answer questions about customer data in a conversational assistant.
11. Detect trends and changes in feedback over time.
12. Display customizable dashboards with real-time visualizations.
13. Support team comments, assignments and decision notes.
14. Tag and filter feedback for categorization.
15. Create and distribute customizable surveys.
16. Recruit, schedule and incentivize research participants.
17. Sync calendars to import and analyze meeting recordings.
18. Generate written summaries, documents and briefs.
19. Rank features and tasks with prioritization frameworks.
20. Visualize product timelines and milestones on a roadmap.

Everything these tools do, in one app

• Multi-source feedback aggregation Collects customer feedback from various channels into one centralized platform.Found in Cynthia AI, Channels by Dovetail, Feedback. and 2 more
• Automated feedback collection Automatically gathers feedback from channels like email and web forms.Found in Feedback.
• AI-driven theme clustering Automatically groups and categorizes feedback by theme or topic.Found in Dovetail Magic, Channels by Dovetail, CustomerIQ
• Sentiment analysis Analyzes customer emotions and satisfaction levels from feedback.Found in Feedback., UserWise
• Automated summarization Generates concise summaries of interviews, feedback, and documents.Found in Dovetail Magic, Dovetail 3.0, CustomerIQ
• Transcription of audio/video Converts audio and video content into text with speaker detection.Found in Dovetail Magic
• Highlighting important moments Identifies and suggests key moments in data for easy review.Found in Dovetail Magic, Dovetail 3.0
• Sensitive data redaction Removes sensitive or confidential information to protect privacy.Found in Dovetail Magic
• Advanced search Quickly locates relevant information across all data sources.Found in Dovetail Magic, Channels by Dovetail, CustomerIQ
• Interactive query assistant Provides conversational answers to questions about customer data.Found in Cynthia AI, Dovetail 3.0
• Trend detection Identifies and tracks changes in customer feedback over time.Found in Cynthia AI, Feedback Rivers
• Customizable dashboards Displays real-time data visualizations that can be tailored.Found in UserWise, Feedback Rivers
• Collaboration tools Facilitates team discussions and decision-making based on feedback.Found in UserWise, Feedback Rivers, ProductlyAI
• Tagging and filtering Organizes feedback with tags and filters for efficient categorization.Found in Feedback Rivers, Channels by Dovetail
• Survey creation Allows creation of customizable surveys and questionnaires for targeted data gathering.Found in Feedback.
• Participant recruitment Finds, schedules, and incentivizes research participants from a database.Found in Dovetail 3.0
• Calendar integration Syncs with calendars to upload and analyze recordings from meetings.Found in Dovetail 3.0
• AI content generation Generates written content such as summaries, documents, and marketing materials.Found in ivie, CustomerIQ
• Prioritization frameworks Helps teams rank features and tasks effectively.Found in ProductlyAI
• Roadmap planning Visualizes product timelines and milestones.Found in ProductlyAI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted feedback exports, interview recordings, survey responses and support tickets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted feedback exports

4. 3

   Interview recordings

5. 4

   Survey responses and support tickets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final prioritization, roadmap decisions and sensitive-data handling remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connections and imports, Editable insight workspace, Prioritization and roadmap view. Use a project list for feedback sets, a central evidence canvas with theme clusters, and a right-hand panel for tags, sentiment, source links and comments. Let users compare themes across time periods. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant quote or recording. Make the task-specific outcome reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source connections, participant records, consent states, redaction rules, approval states, usage allowances, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned feedback exports, authorized interview recordings and permitted research sources. Cloud storage, calendar systems, survey tools and product management destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: aggregate feedback from email, web forms, support tickets and review sites; collect feedback automatically from connected channels. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product teams collecting feedback and research data across channels use it to solve "feedback and research data sit in separate tools, so teams cannot see themes, sentiment or priorities without manual consolidation"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted insights per research hour and decisions traced to source evidence.
4. Measure, then decide. Track accepted insights per research hour and decisions traced to source evidence; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One approved feedback source and one recording format; final prioritization and sensitive-data decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate feedback from email, web forms, support tickets and review sites; collect feedback automatically from connected channels. Support the remaining modules with operator review: cluster feedback into themes with AI assistance; score sentiment and satisfaction per item and theme; summarize interviews, documents and feedback threads. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence. Retain the explicit scope boundary: One approved feedback source and one recording format; final prioritization and sensitive-data decisions remain human.

What the build depends on. Source upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity analysis requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved feedback source and one recording format; final prioritization and sensitive-data decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined feedback package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual consolidation while keeping every insight traceable to its source. Demonstrate a concrete reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product teams collecting feedback and research data across channels professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence from a small authorized input set, with a transparent calculation of accepted insights per research hour and decisions traced to source evidence and no promised savings.

The first 30 days

1. Week 1: interview five product teams collecting feedback and research data across channels and inspect a recent example of feedback and research data sit in separate tools, so teams cannot see themes, sentiment or priorities without manual consolidation.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted insights per research hour and decisions traced to source evidence, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted insights per research hour and decisions traced to source evidence. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted insights per research hour and decisions traced to source evidence; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved themes, prioritization rules and review examples, together with reliable delivery for a narrow product-research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product teams collecting feedback and research data across channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Dovetail Magic, Cynthia AI, Channels by Dovetail, ivie, Feedback., UserWise, Dovetail 3.0, Feedback Rivers, ProductlyAI and CustomerIQ. Compare this product with the buyer's present method on accepted insights per research hour and decisions traced to source evidence. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Transcription, storage, model calls, reviewer hours, participant incentives and client revision rounds. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved themes, sentiment summaries and prioritized opportunities linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve participant consent, source attribution, quotation accuracy and usage permissions. Customers approve substantive changes and publication scope. One approved feedback source and one recording format; final prioritization and sensitive-data decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.